Proliferation of large-scale image collections on web has made the task of efficient image retrieval challenging.
Given a query image or region, the goal is to retrieve images of the same object or scene from a large scale database with high accuracy, efficiency and less memory usage. One of the core problems is how to concisely represent the visual information present in images. A number of methods have been proposed recently that address this issue from both computational efficiency as well as retrieval accuracy perspectives. However, there is a growing need for algorithms that can achieve reasonable trade-offs on both these aspects. Vector of Locally Aggregated Descriptors (VLAD) [1]} proposed by Jégou et al. is one of the seminal contributions in this area as they show that compact and accurate VLAD representation is able to scale to billions of descriptors (by avoiding expensive hard disk operations) and still retain superior retrieval performance. However, one of the limitations of this representation is its inability to incorporate more keypoint level information that can potentially lead to enhanced performance. One such information is the dominant angle of the detected keypoint, also referred to as “Keypoint Angle”, which is often discarded for the sake of obtaining rotational invariance in matches.
A toy example is illustrated in Figure REF , in which VLAD is unable to differentiate between the configurations shown in the left and right figures where keypoints (red dots) differ only in their orientations, while having same descriptor representation and distance in the feature space towards the centroid \(c_i\) .
Thus, we hypothesize that keypoint angles provide useful geometric cues which can be very useful for matching images. Integrating this information in a principled way can substantially improve the performance of existing VLAD based representation. In this paper, we present Geometric VLAD (gVLAD) that strengthens the VLAD representation by incorporating weak geometric cue in form of keypoint angles.
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